This paper aims to evaluate the solution behaviour of the "Overall Equipment Effectiveness" to determine the performance in innovative manufacturing environments. OEE literature categorizes six major losses: "Equipment failures and breakdowns", "Setup/Adjustments", "Idling and minor Stops", "Reduced Speed Losses", "Reduced Yields and Quality Defects and Rework". The technical implementation method used by the authors to achieve their scope has been described using a hybrid of software development programs such as: SAP MII, SAP PCo and SAP ME in relation to the theoretical existing principles. The results presented conclude that losses in practice can occur at both high rates and low rates and therefore, an innovative manufacturing concept must be further enhanced. In terms of performance related to equipment utilization, the specific stoppages and downtimes need to be taken in consideration and adjust the OEE results accordingly. The actual situation in a real-life environment has increased in efficiency, but as technology improves, opportunities for further development appear at a high rate.
The Automated Guided Vehicle (AGV) is a mobile device that is used in the manufacturing plants lately for transporting materials from one space to another. AGVs are connected to a central navigation system which continuously directs the device to its source or destination. Their main features are their flexibility and adaptability to the environment once the AGVs are configured. The main focus of this article is to present the AGV technology based on vision navigation system reinforced by programming code and navigation graphs with the scope of adhering to the latest innovative concepts in terms of efficiency and optimized manufacturing. The topic of vision-based systems being recent to the engineering evolution, several academicians have discussed probabilities for improvement: some introduce the concept of an independent robot which can learn through artificial intelligence about the dynamic environment or others state, that a neural network can be used in the future as the basis for movement of the AGV in the workspace.
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